UNIVERSIDADE FEDERAL DE CIÊNCIAS DA SAÚDE DE PORTO ALEGRE – UFCSPA
CURSO DE PÓS-GRADUAÇÃO EM CIÊNCIAS DA SAÚDE
André Zuanazzi Dornelles
Acurácia de variáveis antropométricas e sua associação com marcadores lipídicos para identificar risco para
doenças cardiovasculares em população de idade escolar do
município de Sapucaia do Sul.
Porto Alegre
2015
André Zuanazzi Dornelles
Acurácia de variáveis antropométricas e sua associação com marcadores lipídicos para identificar risco para
doenças cardiovasculares em população de idade escolar do
município de Sapucaia do Sul.
Dissertação submetida ao Programa de Pós-Graduação em Ciências da Saúde da Fundação Universidade Federal de Ciências da Saúde de Porto Alegre como requisito para a obtenção do grau de Mestre.
Orientadora: Prof. Dra. Márcia Regina Vitolo Co-orientadora: Prof. Dra. Liane Nanci Rotta
Porto Alegre
2015
DEDICATÓRIA
Dedico este trabalho aos meus pais, Luis Ricardo Dornelles e Estela Zuanazzi Dornelles, que não somente me proporcionaram todas as condições socioeconômicas para me educar, como me motivaram com muito amor, empenho e exemplo para me desenvolver como pessoa. Dedico também ao meu irmão Gabriel Zuanazzi Dornelles, por compartilharmos todas as etapas de nossa vida juntos, e que assim continue.
“O maior inimigo do conhecimento não é a ignorância, mas a ilusão de
conhecimento” Stephan Hawking.
Agradecimentos
Aos meus pais Luis Ricardo Dornelles e Estela Zuanazzi Dornelles, ao meu irmão Gabriel Zuanazzi Dornelles pelo amor, presença, exemplo e, obviamente, brigas que me motivaram a seguir esse caminho.
À minha eterna avó Sara Edila Zuanazzi, sempre presente em memória como maior exemplo de bondade e amor inquestionável que posso me fundamentar.
À minha tia Vera Regina Dornelles, minha segunda mãe que estará sempre em meu coração.
À minha família: Marcelo Zuanazzi, Clarissa Dornelles, Denise Dornelles e Eduardo Dornelles.
Ao Zanini Futebol Clube: Guilherme Rabello Neves, Juliano Mirapalheta Sangoi, Ricardo Abreu, Matheus Jaeger, Lucas Ughini, Eduardo Cardoso, Gustavo Guerses e Lucas Cunha pela irmandade desde a infância.
Agradeço à minha orientadora, Márcia Regina Vitolo, pelos ensinamentos (pessoais e acadêmicos), orientações, exemplo e dedicação. Hoje posso dizer que entendo e fui capaz de sentir o papel de uma orientadora e pesquisadora para o desenvolvimento de um aluno e de um projeto.
À minha co-orientadora, Liane Nanci Rotta, pelo desenvolvimento, contribuição e acolhimento nessa etapa.
Agradeço às colegas do Núcleo de Pesquisa em Nutrição. Em especial às amigas Caroline Sangalli e Paula Leffa, pelos ensinamentos e amizade durante esse período. À Cintia Costa Gama, à Vivian Rodriguiez, à Lovaine Rodrigues, à Camila Dallazen e à Gislaine Anastácio. Que nossa relação permaneça com o tempo.
Agradeço em especial aos colegas de PPG Ramiro Nunes, como exemplo de conduta para a vida, Giuseppe Potrick Stefani, como motivador e amigo, Jadson Pereira Alves e Edson Quagliotto, pelos ensinamentos e amizade.
Agradeço ao Programa de Pós-Graduação em Ciências da Saúde e à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) pelo suporte financeiro, auxílio e apoio concedido, que foi de fundamental importância para o desenvolvimento deste trabalho.
Agradeço a todos aqueles que contribuíram de alguma forma na realização
desse trabalho e dessa etapa.
SUMÁRIO
LISTA DE ABREVIATURAS...5
1.RESUMO...6
2.ABSTRACT...7
3.INTRODUÇÃO...8
3.1 Marcadores antropométricos e risco cardiovascular...9
3.2 Dislipidemia e risco para a saúde em escolares...12
4.REFERÊNCIAS...14
5.OBJETIVOS...20
5.1 Objetivo geral...20
5.2 Objetivos específicos...20
6.ARTIGO ORIGINAL...21
6.1 Cover Letter...21
6.2 Abstract...23
6.3 Introduction...24
6.4 Methods...25
6.5 Results...27
6.6 Discussion...28
6.7 Acknowledgements...31
6.8 References...31
6.9 Appendices...34
6.10 Tables, Figure legends...37
7.CONCLUSÃO...38
8.ANEXOS...39
8.1 Aceite do Comitê de Ética em Pesquisa...39
8.2 Normas de submissão à Revista: Nutrition, Metabolism & Cardiovascular
Diseases...40
LISTA DE ABREVIATURAS
DALY: Disability-Adjusted Life Year;
DAC: Doença Arterial Coronariana;
DCNT: Doenças Crônicas não Transmissíveis;
ONU: Organização das Nações Unidas;
IMC: Índice de Massa Corporal;
CC: Circunferência da Cintura;
RCE: Relação cintura/estatura;
P/E: Peso para estatura;
OMS: Organização Mundial da Saúde;
TG: Triglicerídeos;
CT: Colesterol Total;
HDL-c: Lipoproteína de alta densidade;
LDL-c: Lipoproteína de baixa densidade;
IHCIS: Integrated HealthCare Information Services;
BHS: Bogalusa Heart Study;
HAS: Hipertensão Arterial Sistêmica;
AUC: Area Under the Curve;
ROC: Reciever Operating Characteristic;
SPSS: Statistical Package for the Social Sciences;
ANOVA: One-way Analysis of Variance.
1.RESUMO
Introdução: Alterações no perfil lipídico desempenham um papel fundamental no desenvolvimento de risco cardiovascular. Ferramentas antropométricas são utilizadas como medidas para identificar excesso de peso e obesidade central, os quais são associados de maneira positiva com essas alterações. No entanto, é necessário um maior esclarecimento sobre a aplicabilidade destas ferramentas de acordo com o sexo e faixas etárias.
Objetivo: Comparar a acurácia do Índice de Massa Corporal (IMC), da circunferência da cintura (CC) e da Relação cintura/estatura (RCE) para identificar o risco para doenças cardiovasculares de uma população em idade escolar de Sapucaia do Sul, Rio Grande do Sul, Brasil.
Métodos: 618 crianças em idade escolar (6-17 anos) de 11 escolas municipais foram coletadas. Dessas, 592 crianças foram usadas para a análise final, divididas em quatro grupos: meninos 6-11 anos; meninos de 12 a 17 anos; meninas de 6 a 11 anos; meninas de 12 a 17 anos. Os desempenhos das ferramentas antropométricas foram avaliados de acordo com área sob a curva (AUC) em Reciever Operating Characterisct (ROC) com base em duas ou mais alterações no perfil lipídico (colesterol total, triglicerídeos, HDL-colesterol ou LDL-colesterol). Pontos de corte ótimos foram extraídos à partir do índice de Youden.
Resultados: A AUC para os grupos foram: 6-11 meninos (0,735 ± 0,05 para o IMC, 0,728 ± 0,06 para CC e 0,719 ± 0,06 para RCE) e 12-17 (0,855 ± 0,05 para o IMC, 0,872 ± 0,04 para 0,902 e CC ± 0,04 para RCE); meninas 6-11 (0,704 ± 0,06 para o IMC, 0,661 ± 0,06 para CC e 0,696 ± 0,05 para RCE) e 12-17 (0,635 ± 0,08 para o IMC, 0,712 ± 0,06 para CC e 0,702 ± 0,06 para RCE). De acordo com os melhores desempenhos na AUC, os pontos de corte ótimos foram: meninos 6-11 (0,74 para o IMC e 63,25 para CC) e 12-17 (0,5 para RCE); meninas 6-11 (1,715 para o IMC e 0,49 para RCE) e 12-17 (71,25 por CC e 0,45 por RCE).
Conclusão: As três ferramentas antropométricas foram adequadas para identificar
risco cardiovascular em crianças de idade escolar. De maneira geral, RCE é um
índice melhor e mais simples.
2.ABSTRACT
Background: Alterations in lipid concentrations play a fundamental role in the development of cardiovascular risk. Anthropometric tools are used as a measure of excessive weight and central obesity, which are positively associated with these alterations. However, applicability of these tools needs more exploration according to sex and age groups.
Aim: Compare the accuracy of Body-mass-index (BMI), Waist Circumference (WC) and Waist to Height Ratio (WHtR) to identify cardiovascular disease risk of a school age population from Sapucaia do Sul, Rio Grande do Sul, Brazil.
Methods: 618 children of school age (6-17 years) from 11 municipal schools were collected, 592 children were used for the final analysis divided into four groups: boys from 6 to 11 years; boys from 12 to 17 years; girls from 6 to 11 years; girls from 12 to 17 years. Anthropometric tools’ performance were evaluated according to Area Under the Operating Characteristic Curve (AUC) based on two or more alterations on lipid profile (Total Cholesterol, Triglycerides, HDL-cholesterol, LDL-cholesterol).
Optimal cut-off points were extracted using Youden Index.
Results: The AUC for the groups were: boys 6-11 (0.735 ±0.05 for BMI, 0.728 ±0.06 for WC and 0.719 ±0.06 for WHtR) and 12-17 (0.855 ±0.05 for BMI, 0.872 ±0.04 for WC and 0.902 ±0.04 for WHtR); girls 6-11 (0.704 ±0.06 for BMI, 0.661 ±0.06 for WC and 0.696 ±0.05 for WHtR) and 12-17 (0.635 ±0.08 for BMI, 0.712 ±0.06 for WC and 0.702 ±0.06 for WHtR). According to best performances in the AUC, optimal cut-off points were: boys 6-11 (0.74 for BMI and 63.25 for WC) and 12-17 (0.5 for WHtR);
girls 6-11 (1.715 for BMI and 0.49 for WHtR) and 12-17 (71.25 for WC and 0.45 for WHtR).
Conclusion: The three anthropometric tools were adequate to identify risk for
cardiovascular risk in school age children. Overall, WHtR is a better and simpler
index.
3.INTRODUÇÃO
O aumento da prevalência do excesso de peso e da obesidade pode ser descrito como uma pandemia (ROTH et al., 2004; SWINBURN et al., 2011; POPKIN et al., 2012). Em 2010, estimou-se que sobrepeso e obesidade causaram 3-4 milhões de mortes, 4% de anos de vida perdidos e 4% Disability-Adjusted Life Year (DALYs – ajuste de anos de vida perdidos correlacionado com anos vividos com incapacidades) (LIM et al., 2012). Mundialmente, a prevalência de sobrepeso e obesidade combinados subiu 27,5% para adultos e 47,1% para crianças entre 1980 e 2013. Em números absolutos, essa população cresceu de 857 milhões para 2,1 bilhões de indivíduos no mesmo período de tempo (NG et al., 2014). Esse cenário atual sugere que, indubitavelmente, o incremento da obesidade pode causar uma queda na expectativa de vida (OLSHANSKY et al., 2005).
No Brasil, esse cenário se apresenta de maneira preocupante, visto que a prevalência do sobrepeso ultrapassa mais de 50% da população, em regiões urbanas. Nas 27 capitais dos estados nacionais, a frequência de excesso de peso é de 50,8%, com maior prevalência em homens (54,7%), do que mulheres (47,4%).
Foi encontrada prevalência de excesso de peso em 62% dos homens de Porto Alegre e em 52% das mulheres de Manaus. Nessa mesma amostra, é possível identificar 17,5% de indivíduos com obesidade, com taxas mais elevadas nas cidades de Macapá, para homens (23%), e de Cuiabá, para mulheres (23%)(SAÚDE, 2014).
O sobrepeso e obesidade infantis variam de acordo com a região territorial e faixa etária. No Sul do país, o sobrepeso representa 25,7% de crianças e adolescentes de 6 a 18 anos; enquanto isso, 10,4% dessa população é obesa. Na região Sudeste, 13,7% da população entre 2 e 19 anos apresenta sobrepeso e 15,4% obesidade. No Nordeste do Brasil, foi identificado 15,8% de sobrepeso e 4,3% de obesidade na faixa etária de 6 a 19 anos. O Norte apresenta prevalência de 28,8% de sobrepeso entre 6 e 19 anos. Já a região Central apresenta 16,8% de sobrepeso e 5,3% de obesidade na população de 6 a 10 anos de idade (NIEHUES et al., 2014). O excesso de peso em crianças e adolescentes está em ascendência, principalmente após os 5 anos, em todas as regiões e classes econômicas do país.
Na faixa etária dos 5 aos 9 anos, 33,5% das crianças apresentam excesso de peso.
Além disso, 16,6% dos meninos e 11,8% das meninas estão obesos nessa idade (IBGE, 2010).
O excesso de peso e a obesidade estão diretamente associados ao comprometimento cardiovascular, que se apresenta como a principal causa de morte prematura no mundo (PAGIDIPATI & GAZIANO, 2013; GO et al., 2014). O desenvolvimento das doenças cardiovasculares está associado a inúmeros fatores de risco, como o consumo alimentar e níveis lipídicos plasmáticos, com início ainda quando criança (EXPERT PANEL ON INTEGRATED GUIDELINES FOR CARDIOVASCULAR et al., 2011; MORRISON et al., 2012). O hábito alimentar do indivíduo sofre influências na infância e persiste como base para os padrões alimentares na vida adulta (NICKLAUS et al., 2013).
De acordo com Brotons e colaboradores (BERENSON et al., 1998), o nível de colesterol na infância é um fator preditivo do nível de colesterol na vida adulta. O inicio da Doença arterial coronariana (DAC) na infância, pelo aumento do colesterol plasmático, seria potencializado no decorrer da vida pela obesidade e por uma série de outros fatores, como história familiar, inatividade física e hipertensão arterial (MCGILL et al., 2000). A partir desses dados, é possível inferir a necessidade de prevenção dos fatores de risco o mais cedo o possível.
No âmbito nacional, para deter a evolução de fatores de risco associados à obesidade, o Governo Federal lançou em 2011 o Plano de Ações Estratégicas para o Enfrentamento das Doenças Crônicas não Transmissíveis (DCNT). Essa iniciativa é parte do grande projeto da Organização Mundial da Saúde (OMS), vinculado a países componentes da Organização das Nações Unidas (ONU), com ênfase à Estratégia Global da OMS em Dieta, Atividade Física e Saúde. Essa iniciativa representa a importância do aprimoramento do rastreamento da condição de excesso de peso e obesidade e do desenvolvimento de intervenções frente ao cenário atual brasileiro e mundial.
3.1 Marcadores antropométricos e risco cardiovascular em escolares
O sobrepeso na infância é fator de risco para obesidade mórbida no decorrer da
vida (FERRARO et al., 2003). De maneira isolada, a obesidade está associada com
morbidade e mortalidade de inúmeras doenças, como doenças cardiovasculares, câncer e diabetes (AMUNDSON et al., 2010).
A mensuração do excesso de peso e a análise de sua repercussão pode ser dar por marcadores antropométricos. A mensuração do excesso de peso e a análise de sua repercussão pode ser dar por marcadores antropométricos. A relação cintura/estatura (RCE) foi proposta por McCarthy e Ashwell, em 2006 (MCCARTHY
& ASHWELL, 2006) como uma ferramenta antropométrica alternativa que leva em consideração tanto crescimento longitudinal como adiposidade central. A mensagem central por trás dessa relação é simples: “Mantenha sua circunferência da cintura a menos da metada da sua altura”. Esse índice pode ser utilizado para rastrear mudanças relevantes na composição corporal e já foi utilizado para identificar risco para doenças crônicas em adultos e, em alguns casos, em crianças (MAFFEIS et al., 2008; LICHTASH et al., 2013).
Dentre as ferramentas antropométricas, é comum a utilização do Índice de Massa Corporal (IMC) e da circunferência da cintura (CC), não obstante, ainda há a necessidade de esclarecer a acurácia da RCE (KAHN et al., 2005; MCCARTHY &
ASHWELL, 2006; FREEDMAN et al., 2007). Os três marcadores apresentam necessidade de padronização de treinamento por parte dos avaliadores para poderem ser comparados, porém requerem baixos recursos financeiros para sua reprodutibilidade (LICHTASH et al., 2013). O IMC, que apresenta incremento em crianças e adultos nos anos recentes, se associa de maneira adequada com o grau de adiposidade e pode ser utilizado como preditor de DAC em jovens adultos (JANSSEN et al., 2005). Por outro lado, não é capaz de diferenciar a adiposidade subcutânea da adiposidade visceral, um fator de predição para doenças crônicas não transmissíveis (BIGAARD et al., 2005).
O IMC nem sempre foi usado para identificar sobrepeso e obesidade em
crianças. Inicialmente, estas condições eram definidas usando a relação peso para
estatura (P/E) de acordo com escores-Z para idade, com o objetivo de identificar as
crianças mais pesadas para uma dada altura e idade (Use and interpretation of
anthropometric indicators of nutritional status. WHO Working Group, 1986; Physical
status: the use and interpretation of anthropometry. Report of a WHO Expert
Committee, 1995). Essa ferramenta continua a ser utilizada como um indicador mais
sensível da obesidade que o IMC (STANOJEVIC et al., 2007). No entanto, a
implementação do P/E se demonstrou complicada, uma vez que não há um valor
único que pode ser usado para crianças de todas as idades. Além disso, essa abordagem não é utilizada em adultos e é menos familiar do que o IMC, tanto pelos profissionais de saúde como pelo público em geral. Uma medida única, ou índice, ajustado para a altura é preferível, e pesquisadores e clínicos têm utilizado o IMC para facilitar esse processo. Embora os pontos de corte para IMC serem amplamente utilizados nas definições de sobrepeso e obesidade infantil na literatura, apenas recentemente eles foram oficializados pela OMS (DE ONIS & LOBSTEIN, 2010). Historicamente, o IMC foi desenvolvido a partir de uma população de adultos diabéticos, a fim de comparar o peso individual com suas respectivas estaturas, e foi demonstrado como um bom preditor de tecido adipose (DOAK, 2013). Os pontos de corte de 25 e 30 kg/m² foram então atribuídos a partir de uma associação de maior risco de mortalidade a essas condições (ASHWELL, 2011). Devido a possíveis erros de classificação ao utilizar esse índice em crianças, é fundamental avaliar criticamente o uso do IMC para diagnosticar o sobrepeso ou a obesidade.
É possível encontrar diferentes metodologias para mensurar a CC na literatura (RUDOLF et al., 2007). Sendo assim, métodos específicos podem comprometer a comparação dessa medida entre estudos e populações diferentes (MAGALHAES et al., 2014). Por exemplo, é possível encontrar autores como Schwandt (SCHWANDT et al., 2008) e Roswall (ROSWALL et al., 2009), que utilizaram o ponto médio entre a crista ilíaca e a costela inferior, enquanto Taylor (TAYLOR et al., 2008) mediram a CC na menor circunferência entre a crista ilíaca e a caixa torácica. Como esses referenciais antropométricos podem complicar a interpretação dos resultados das medidas individuais de CC, não é claro se essas diferenças podem gerar algum viés sistemático entre os estudos.
Devido à inexistência de um ponto de corte universal para a CC para crianças
(WHO, 2008), a RCE se apresenta como marcador antropométrico interessante, com
recomendação de valor desejado abaixo de 0,5 (MCCARTHY & ASHWELL, 2006). A
RCE foi considerada útil para identificar risco metabólico em crianças com excesso
de peso (MAFFEIS et al., 2008), porém estudos nessa população permanecem
escassos e necessários para a fundamentação dessa ferramenta (CAMPAGNOLO
et al., 2011). Dessa maneira, persiste a dúvida relativa a um marcador
antropométrico com adequada sensibilidade e especificidade para avaliar risco para
doenças cardiovasculares na idade escolar.
3.2 Dislipidemia e risco para a saúde em escolares:
Dislipidemia pode ser caracterizada pelo incremento nos níveis séricos de triglicerídeos (TG) e/ou colesterol total (CT) ou diminuição nos níveis séricos da lipoproteína de alta densidade (HDL-c), que pode levar ao desenvolvimento de aterosclerose. Os critérios para determinação de alterações desses parâmetros, segundo a V Diretriz Brasileira de Dislipidemias e Prevenção de Aterosclerose, preconizam valores elevados de CT, TG e lipoproteína de baixa densidade (LDL-c) em ≥ 170 mg/dL, ≥ 130mg/dL e ≥ 130mg/dL, respectivamente; valores desejáveis de HDL-c são determinados em ≥ 45mg/dL para a idade de 2 – 19 anos (SOCIEDADE BRASILEIRA DE et al., 2013). Em concordância com o aumento da prevalência de excesso de peso em escolares, é possível identificar inadequação desses parâmetros para a mesma idade. Li e colaboradores avaliaram 273.064 crianças americanas com registro de ao menos um desses parâmetros lipídicos na plataforma Integrated HealthCare Information Services (IHCIS). Nessa amostra, 22,9% dos indivíduos apresentaram dislipidemia, com elevada trigliceridemia como alteração mais comum (13,2%) (LI et al., 2010). Esse achado está de acordo com o apresentado em outro grande estudo populacional (JOHNSON et al., 2009), capaz de rastrear baixos níveis de HDL-c em adolescentes do sexo masculino quando comparados a meninos mais jovens.
A relevância do rastreamento do perfil lipídico em diferentes faixas etárias é demonstrada no Bogalusa Heart Study (BHS): um estudo epidemiológico da história natural de fatores de risco cardiovasculares e ambientais, acompanhados desde a infância. Composto por uma amostra birracial, relativamente estável, de todas as crianças e jovens adultos residentes da região semi rural de Bogalusa, Louisiana, desde 1972 (BERENSON et al., 1982). Mais de 160 estudos foram conduzidos até o momento, com o esclarecimento que fatores de risco cardiovasculares são identificáveis na infância e são preditivos para risco para DAC na idade adulta (BURKE et al., 1986; BERENSON, 2002).
Estudos observacionais demonstram claramente que a maioria das etiologias
para doenças cardiovasculares tem seu início da infância. De maneira mais
específica, é possível encontrar alterações anatômicas tão precocemente quanto de
5 a 8 anos de idade do indivíduo. Estudos de imagens do coração, das artérias
carótida e femoral demonstram que DAC e hipertensão arterial sistêmica (HAS) pode
se iniciar precocemente na vida (PAUL et al., 2011) e estudos de autópsia
confirmam lesões na aorta e em veias coronárias nessa faixa etária (TRACY et al.,
1995). Além disso, indivíduos de raça negra apresentam uma forma mais grave e
prevalente de HAS e diabetes (CHEN et al., 2011; NGUYEN et al., 2012), homens
brancos podem apresentar DAC de maneira precoce (BERENSON & BOGALUSA
HEART STUDY, 2001) e mulheres apresentam um retardamento para desenvolver
doenças cardiovasculares (KONES, 2011).
4.REFERÊNCIAS
AMUNDSON, D. E., et al. The obesity paradox. Crit Care Clin, v.26, n.4, p.583-96.
Oct, 2010.
ASHWELL, M. Charts Based on Body Mass Index and Waist-to-Height Ratio to Assess the Health Risks of Obesity: A Review. The Open Obesity Journal, v.3, p.78-84, 2011.
BERENSON, G. S. Childhood risk factors predict adult risk associated with
subclinical cardiovascular disease. The Bogalusa Heart Study. Am J Cardiol, v.90, n.10C, p.3L-7L. Nov 21, 2002.
BERENSON, G. S. & I. BOGALUSA HEART STUDY. Bogalusa Heart Study: a long- term community study of a rural biracial (Black/White) population. Am J Med Sci, v.322, n.5, p.293-300. Nov, 2001.
BERENSON, G. S., et al. Cardiovascular risk factors in children. Should they concern the pediatrician? Am J Dis Child, v.136, n.9, p.855-62. Sep, 1982.
BERENSON, G. S., et al. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N Engl J Med, v.338, n.23, p.1650-6. Jun 4, 1998.
BIGAARD, J., et al. Waist circumference and body composition in relation to all- cause mortality in middle-aged men and women. Int J Obes (Lond), v.29, n.7, p.778- 84. Jul, 2005.
BURKE, G. L., et al. Cardiovascular risk factors and their modification in children.
Cardiol Clin, v.4, n.1, p.33-46. Feb, 1986.
CAMPAGNOLO, P. D., et al. Waist-to-height ratio as a screening tool for children
with risk factors for cardiovascular disease. Ann Hum Biol, v.38, n.3, p.265-70. May,
2011.
CHEN, W., et al. Adult hypertension is associated with blood pressure variability in childhood in blacks and whites: the bogalusa heart study. Am J Hypertens, v.24, n.1, p.77-82. Jan, 2011.
DE ONIS, M. & T. LOBSTEIN. Defining obesity risk status in the general childhood population: which cut-offs should we use? Int J Pediatr Obes, v.5, n.6, p.458-60.
Dec, 2010.
DOAK, C. H., DANIEL; NORRIS, SHANE; PONCE, MAIZA; POLMAN, KATJA;
GRIFFITHS, PAULA. Is body mass index an appropriate proxy for body fat in children? Global Food Security, v.2, p.65-71, 2013.
EXPERT PANEL ON INTEGRATED GUIDELINES FOR CARDIOVASCULAR, H., et al. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics, v.128 Suppl 5, p.S213-56.
Dec, 2011.
FERRARO, K. F., et al. The life course of severe obesity: does childhood overweight matter? J Gerontol B Psychol Sci Soc Sci, v.58, n.2, p.S110-9. Mar, 2003.
FREEDMAN, D. S., et al. Relation of body mass index and waist-to-height ratio to cardiovascular disease risk factors in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr, v.86, n.1, p.33-40. Jul, 2007.
GO, A. S., et al. Heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation, v.129, n.3, p.e28-e292. Jan 21, 2014.
IBGE, I. B. D. G. E. E. Pesquisa de Orçamentos Familiares, 2008/2009. 2010.
JANSSEN, I., et al. Utility of childhood BMI in the prediction of adulthood disease:
comparison of national and international references. Obes Res, v.13, n.6, p.1106-15.
Jun, 2005.
JOHNSON, W. D., et al. Prevalence of risk factors for metabolic syndrome in adolescents: National Health and Nutrition Examination Survey (NHANES), 2001- 2006. Arch Pediatr Adolesc Med, v.163, n.4, p.371-7. Apr, 2009.
KAHN, H. S., et al. A population-based comparison of BMI percentiles and waist-to- height ratio for identifying cardiovascular risk in youth. J Pediatr, v.146, n.4, p.482-8.
Apr, 2005.
KONES, R. Primary prevention of coronary heart disease: integration of new data, evolving views, revised goals, and role of rosuvastatin in management. A
comprehensive survey. Drug Des Devel Ther, v.5, p.325-80, 2011.
LI, J., et al. Prevalence of pediatric dyslipidemia: comparison of a population-based claims database to national surveys. Pharmacoepidemiol Drug Saf, v.19, n.10, p.1031-40. Oct, 2010.
LICHTASH, C. T., et al. Body adiposity index versus body mass index and other anthropometric traits as correlates of cardiometabolic risk factors. PLoS One, v.8, n.6, p.e65954, 2013.
LIM, S. S., et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet, v.380, n.9859, p.2224-60. Dec 15, 2012.
MAFFEIS, C., et al. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. J Pediatr, v.152, n.2, p.207-13. Feb, 2008.
MAGALHAES, E. I., et al. [Waist circumference, waist/height ratio, and neck circumference as parameters of central obesity assessment in children]. Rev Paul Pediatr, v.32, n.3, p.273-81. Sep, 2014.
MCCARTHY, H. D. & M. ASHWELL. A study of central fatness using waist-to-height
ratios in UK children and adolescents over two decades supports the simple
message--'keep your waist circumference to less than half your height'. Int J Obes (Lond), v.30, n.6, p.988-92. Jun, 2006.
MCGILL, H. C., JR., et al. Origin of atherosclerosis in childhood and adolescence.
Am J Clin Nutr, v.72, n.5 Suppl, p.1307S-1315S. Nov, 2000.
MORRISON, J. A., et al. Risk factors for cardiovascular disease and type 2 diabetes retained from childhood to adulthood predict adult outcomes: the Princeton LRC Follow-up Study. Int J Pediatr Endocrinol, v.2012, n.1, p.6, 2012.
NG, M., et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, v.384, n.9945, p.766-81. Aug 30, 2014.
NGUYEN, Q. M., et al. Correlates of age onset of type 2 diabetes among relatively young black and white adults in a community: the Bogalusa Heart Study. Diabetes Care, v.35, n.6, p.1341-6. Jun, 2012.
NICKLAUS, S., et al. Caloric Compensation and Eating in the Absence of Hunger in Early Childhood: Impact of Parental Feeding Practices. Annals of Nutrition and Metabolism, v.63, p.40-40, 2013.
NIEHUES, J. R., et al. Prevalence of overweight and obesity in children and
adolescents from the age range of 2 to 19 years old in Brazil. Int J Pediatr, v.2014, p.583207, 2014.
OLSHANSKY, S. J., et al. A potential decline in life expectancy in the United States in the 21st century. N Engl J Med, v.352, n.11, p.1138-45. Mar 17, 2005.
PAGIDIPATI, N. J. & T. A. GAZIANO. Estimating deaths from cardiovascular
disease: a review of global methodologies of mortality measurement. Circulation,
v.127, n.6, p.749-56. Feb 12, 2013.
PAUL, T. K., et al. Contrast of the impact of multiple cardiovascular risk factors on the femoral and carotid intima-media thickness in asymptomatic young adults: the
Bogalusa Heart Study. Atherosclerosis, v.216, n.2, p.359-64. Jun, 2011.
Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser, v.854, p.1-452, 1995.
POPKIN, B. M., et al. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev, v.70, n.1, p.3-21. Jan, 2012.
ROSWALL, J., et al. Population-based waist circumference and waist-to-height ratio reference values in preschool children. Acta Paediatr, v.98, n.10, p.1632-6. Oct, 2009.
ROTH, J., et al. The obesity pandemic: where have we been and where are we going? Obes Res, v.12 Suppl 2, p.88S-101S. Nov, 2004.
RUDOLF, M. C., et al. What is the best way to measure waist circumference? Int J Pediatr Obes, v.2, n.1, p.58-61, 2007.
SAÚDE, M. D. Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico. 2014.
SCHWANDT, P., et al. First reference curves of waist circumference for German children in comparison to international values: the PEP Family Heart Study. World J Pediatr, v.4, n.4, p.259-66. Nov, 2008.
SOCIEDADE BRASILEIRA DE, C., et al. [V Brazilian Guidelines on Dyslipidemias and Prevention of Atherosclerosis]. Arq Bras Cardiol, v.101, n.4 Suppl 1, p.1-20.
Oct, 2013.
STANOJEVIC, S., et al. The association between changes in height and obesity in
Chilean preschool children: 1996-2004. Obesity (Silver Spring), v.15, n.4, p.1012-
22. Apr, 2007.
SWINBURN, B. A., et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet, v.378, n.9793, p.804-14. Aug 27, 2011.
TAYLOR, R. W., et al. Waist circumference as a measure of trunk fat mass in children aged 3 to 5 years. Int J Pediatr Obes, v.3, n.4, p.226-33, 2008.
TRACY, R. E., et al. Risk factors and atherosclerosis in youth autopsy findings of the Bogalusa Heart Study. Am J Med Sci, v.310 Suppl 1, p.S37-41. Dec, 1995.
Use and interpretation of anthropometric indicators of nutritional status. WHO Working Group. Bull World Health Organ, v.64, n.6, p.929-41, 1986.
WHO. Waist circumference and waist-hip ratio: report of a WHO expert consultation
p.8-11, 2008.
5.OBJETIVOS
5.1 Objetivo geral:
Avaliar a acurácia de variáveis antropométricas e sua associação com marcadores lipídicos para identificar risco para doenças cardiovasculares de uma população de idade escolar do Município de Sapucaia do Sul.
5.2 Objetivos específicos:
- Avaliar a acurácia do IMC para identificar risco para doenças cardiovasculares em escolares estratificados por idade;
- Avaliar a acurácia da Circunferência da Cintura para identificar risco para doenças cardiovasculares escolares estratificados por idade;
- Avaliar a acurácia da Relação Cintura/Estatura para identificar risco para doenças
cardiovasculares escolares estratificados por idade.
6.ARTIGO ORIGINAL 6.1 Cover Letter:
Title:
Waist-to-height Ratio: simpler and better. Accuracy to identify cardiovascular risk in school age children from Brazil.
Authors names and affiliations:
Author 1:
Dornelles, A.Z. universidade federal de ciências da saúde de porto alegre.
Sarmento Leite St. 245. Porto Alegre, Rio Grande do Sul, Brazil. CEP: 90050-170 Phone: +55 (51) 3303-9000. E-mail: a_dornelles@hotmail.com Celphone: +55 (51) 9665-9559
Present/permanent address: Anita Garibaldi St. 1520, apartment 302. Porto Alegre, Rio Grande do Sul, Brazil. CEP: 90480-200. Phone: +55 (51)3328-6115.
Author 2:
Bueno, C.T. universidade luterana do brasil.
Farroupilha Av. 8001. Canoas, Rio Grande do Sul, Brazil. CEP: 92425-900 Phone:
+55 (51) 3477-4000. E-mail: caroltanski@hotmail.com Celphone: +55 (51) 9256- 5055
Author 3:
Rotta, L.N. universidade federal de ciências da saúde de porto alegre.
Sarmento Leite St. 245. Porto Alegre, Rio Grande do Sul, Brazil. CEP: 90050-170 Phone: +55 (51) 3303-9000. E-mail: lnrotta@ufcspa.edu.br Phone: +55 (51) 3303- 8836
Corresponding author:
Vitolo, M.R. universidade federal de ciências da saúde de porto alegre.
Sarmento Leite St. 245. Porto Alegre, Rio Grande do Sul, Brazil. CEP: 90050-170
Phone: +55 (51) 3303-9000. E-mail: vitolo@ufcspa.edu.br Celphone: +55 (51) 8162-
9929
Keywords: Body Mass Index, Waist Circumference, Whaist-Height Ratio, Child, Cholesterol. HDL, Lipids, Cardiovascular Diseases, Risk.
Acronyms:
BMI: Body Mass Index WC: Waist Circumference WHtR: Waist-to-Height Ratio AUC: Area Under the Curve
ROC: Receiver Operating Characteristic
DALYs: Disability Adjusted Life Years
CVD: Cardiovascular Disease
6.2 Abstract:
(1) Background and Aims: Significant changes in body composition are used to identify cardiovascular risk. However, applicability of anthropometric tools needs more exploration according to sex and age groups. This study analyzed the accuracy of Body-mass-index (BMI), Waist Circumference (WC) and Waist to Height Ratio (WHtR) to identify cardiovascular disease risk of a school age population from Sapucaia do Sul, Rio Grande do Sul, Brazil.
(2) Methods and Results: 592 children from 11 municipal schools were stratified into: boys 6-11 years; boys 12-17 years; girls 6-11 years; girls 12-17 years.
Anthropometric tools’ performance were evaluated according to Area Under the Curve (AUC) for Receiver Operating Characteristic (ROC) distribution based on two or more alterations on lipid profile (Total Cholesterol, Triglycerides, HDL-cholesterol, LDL-cholesterol). Optimal cut-off points were extracted using Youden Index.
AUC were: boys 6-11 (0.735 for BMI, 0.728 for WC and 0.719 for WHtR) and 12-17 (0.855 for BMI, 0.872 for WC and 0.902 for WHtR); girls 6-11 (0.704 for BMI, 0.661 for WC and 0.696 for WHtR) and 12-17 (0.635 for BMI, 0.712 for WC and 0.702 for WHtR). Optimal cut-off points were: boys 6-11 (0.74 for BMI and 63.25 for WC) and 12-17 (0.5 for WHtR); girls 6-11 (1.715 for BMI and 0.49 for WHtR) and 12-17 (71.25 for WC and 0.45 for WHtR).
(3) Conclusion: The three anthropometric tools were adequate to identify risk for
cardiovascular risk in school age children. Overall, WHtR had a better performance to
identify children with lipid profile alterations.
6.3 INTRODUCTION:
The increased prevalence of obesity and overweight is now considered to be a global pandemic (1-3). In 2010, it was estimated that overweight and obesity caused 3-4 million deaths, a loss of 4% loss of life years, and 4% of Disability Adjusted Life Years (DALYs) due to co-morbidities associated with excess body fat mass (4).
Globally, the prevalence of overweight and obesity among adults is 27.5% for adults is 47.1% for children. In absolute numbers, this population has increased from 857 million to 2.1 billion people, from 1980 to 2013 (5). Thus, it is clear that obesity and overweight are serious public health problems and it is important to improve our understanding of how excess body weight increases the risk for chronic disease.
Excess body weight and obesity are positively associated with cardiovascular diseases (CVD), the leading cause of death in the world (6, 7). The development of CVDs is associated with a number of risk factors, such as the consumption of high-fat diets and high levels of plasma lipids, factors that generally begin in childhood (8, 9).
As reported by Berenson et al. (10), the level of cholesterol in infancy is a predictive factor of the cholesterol level in adulthood. Atherosclerosis begins in infancy by the increase of plasma cholesterol and can be accelerated by obesity through a number of factors, such as family history, physical inactivity, and hypertension (11).
Therefore, it is important to intervene as soon as possible to prevent risk factors from becoming more severe or numerous. Yet, it is unclear what anthropometric variable are most associated with abnormal lipid profiles, making the criteria for intervention difficult to determine.
Overweight in infancy is a risk factor for adult obesity and many obese children continue to remain obese as adolescents and adults (12). The assessment of excess body weight and its relationship to other biological risk factors can be accomplished with relatively simple anthropometric markers. Two of the most common anthropometric assessments of overweight are Body Mass Index (BMI) and waist circumference (WC) which may explain part, but not all, of the potential risk for CVD in children.
These two anthropometric tools have presented strong association with fat
mass for children and adults (13). BMI is expressed as kg/m² and is not able to
differentiate whole body adiposity from central adiposity, a strong predictor for the
development of non noncommunicable diseases (14). WC, in other hand, is positively
associated with longitudinal growth and requires specific cut-off values for evaluation, according to sex and age. Thus, there’s no single universal value to measure possible outcomes due alterations in this parameter (15).
Waist-to-Height Ratio has been proposed as an alternative measure that concerns both longitudinal growth and central adiposity (16). As a simple message to
“keep your waist circumference to less than half your height”, this tool can be used to screen relevant changes in body composition and have already been used to identify risk for chronic diseases in adults and children (17, 18). However, how well these three anthropometric tools are able to identify CVD risk in children of different ages and sex have not been well established. Therefore, the aim of this study was to evaluate the accuracy of BMI, WC and WHtR to identify lipid profile alterations in school age children.
6.4 METHODS:
Population:
This study was based on a population of 618 children of school age (6-17 years ) from 11 municipal schools from Sapucaia do Sul city, Rio Grande do Sul. This is derivative from the project called Investigation of Dyslipidemia of a scholar population from Sapucaia do Sul, conducted in 2008. After application of extreme studentized deviate for outliers, according to distribution of anthropometric and lipid variables, 592 children were used for the final analysis.
Logistic
The staff that conducted the data collection was previously trained and informed according to the project’s ethical aspects. To insure tool’s precision, pre-test was applied. Children at school age and regularly matriculated at the selected schools were invited to initiate in the study. Familiar responsible signed the Informed Consent with the supervision of previously trained member of the research’s staff.
Groups were distributed according to age and sex. Children were stratified into four groups: boys from 6 to 11.99 years; boys from 12 to 17.99 years; girls from 6 to 11.99 years; girls from 12 to 17.99 years.
Anthropometry:
Body composition variables were collected by the same trained measurer.
Subject’s weight was measured with no shoes and light clothes on by digital
weighing-machine (Techline®, Brazil) with precision of 100g; Standing height was
measured with a stadiometer (Seca®, Germany) fixated on a straight wall. Subject was positioned with both heels touching the wall; Waist Circumference (WC) was obtained from the narrowest point of the subject’s chest with a non-extensive tape- measure; Waist-to-height Ratio (WHtR) value was obtained from the equation: Waist (cm)/Height (cm); Body-mass-Index (BMI) value was obtained from the equation:
weight (kg)/ height(m)².The definition of overweight and obesity was according to BMI-per-age. Children who presented ≥1 z-score alteration were classified as overweight, and ≥ 2 z-scores alteration were classified as obese.
Blood analysis:
Blood samples were obtained from 12 hour fasting subjects, with a vacuum system from the brachial vein. The material was collected in tubes with sodium fluoride (EDTA conjugated) for plasma and in tubes with no anti-coagulant for serum.
The material was centrifuged at 3,500/10 minutes and analyzed immediately afterwards. Analysis were obtained with automation apparatus Labmax (Labtest, Brazil), after quality control procedures and control and pathologic serum, at Laboratory of Clinics Analysis from Cruzeiro do Sul’s Health Unity – Porto Alegre.
Total Cholesterol (calorimeter enzymatic test), Triglycerides, HDL-cholesterol (precipitation method) and LDL-cholesterol (Friedwald equation) specific doses were measured with Labtest kits.
Statistical Analysis:
Data was analyzed with the software Statistical Package for the Social Sciences (SPSS) version 21.0. In order to evaluate homogeneous distribution, Kolmogorov-Smirnov test was applied. Normal distribution variables were presented as mean and standard deviation, while non-parametric variables as median and interquartile intervals. Qualitative variables were presented as absolute and relative frequencies. One-way Analysis of Variance (ANOVA) was applied to compare the groups according to anthropometric and biochemistry data. Tukey’s post-hoc test was applied to express significant differences among groups, defined as < 5%.
Outliers were handled with the use of extreme studentized deviate, by maximum deviation from the mean.
Receiver operating characteristic (ROC) analysis was used to determine the
performance of anthropometric indices to identify cardiovascular risk. The accuracy
of the anthropometric measures in predicting cardiovascular risk were assessed by
the area under the operating characteristic curve (AUC); a 95% confidence interval
was used (95% CI), based on two or more alterations in the lipid profile. In order to calculate the optimal cut-off point, Youden index (sum of sensitivity and specificity - 1) was used (19).
Ethical aspects:
The current study was submitted and approved by the Ethical and Scientific Committee of UFCSPA (716/08).
6.5 RESULTS:
This study utilized a cross-sectional analysis of 592 low income children and adolescents from 6-17 years old. Among those, 220 boys were evaluated, 141 from 6 to 11 years and 79 from 12 to 17 years; while 372 girls were evaluated, 224 from 6 to 11 years and 148 from 12 to 17 years. A significant number of families were of low- income, as 44.3% had an annual income of under US$3.141.52, which represents a monthly income equal to or lower than the national minimum wage (approximately US$261.79 per month). Mothers spent an average of 6.79 ±2.8 years in school, while fathers spent 7.01 ±2.8 years in school.
The anthropometric and lipid profile stratified by sex and age group are presented in Tables I and II. The prevalence of overweight was 40.5% for the boys and 34.4% for the girls, while 20% of the boys and 12.9% of the girls were classified as obese, according to BMI-for-age (≥ 1 Z-score and ≥2 Z-score, respectively). High Waist/Height ratio (≥0.5) was identified in 28.2% of the boys and 29.6% of the girls.
Central obesity was identified in 22.2% of the boys and in 25.4% of the girls, according to WC for age (≥90th percentile). Boys showed lower levels of HDL- cholesterol at 12-17 years when compared to 6-11 years (48.7 ±12 vs. 43.9 ±9.4) and when compared to girls at 12-17 years (48.7 ±10.9 vs. 43.9 ±9.4). Boys presented higher Triglycerides alterations at both age groups, when compared to girls (8.5% vs.
5.8% at 6-11 and 11.4% vs 6.1% at 12-17) At least one lipid profile alteration was detected in 42.7% of the boys and in 41.4% of the girls. Risk for cardiovascular diseases was identified in 11.8% of the boys and 10.5% of the girls (which presented two or more alterations in the lipid profile).
The AUC for BMI, WC and WHtR are presented in Table III, according to sex
and age group. AUC was higher for all anthropometric indices in boys when
compared to girls, stratified by age group (Table III; Figure 1).
The optimal cut-off values of the anthropometric indices found to predict children and adolescents with two or more cardiovascular risk factors using the ROC curve analysis are summarized in Table IV.
6.6 DISCUSSION:
As the prevalence of overweight and obesity increases around the globe, especially in children, effective tools are needed for screening early stages of possible outcomes from this condition (5). Anthropometric variables are simple and low cost tools that are able to identify relevant changes in body composition that can be considered of risk for chronic diseases in children and adults (20). Briefly, in our cross-sectional study, we found that WHtR was the most accurate anthropometric marker for increased cholesterol and triglycerides, HDL, and LDL in children of 6-17 years of age. Thus, it may be proposed that using an index that incorporates both central body size and growth may be better markers for CVD risk than traditional markers, such as BMI or WC.
In a meta-analysis conducted by (21), twenty-four cross-sectional studies and ten prospective studies with a total number of 512.809 adult participants were identified. WHtR was found to have a stronger association than BMI with diabetes mellitus (rRR: 0.71, 95% CI: 0.59-0.84) and metabolic syndrome (rRR: 0.92, 95% CI:
0.89-0.96) in cross-sectional studies. In prospective studies, WHtR was superior to BMI in detecting several outcomes, including CVD incidence and mortality, as well as all-cause mortality. BMI did not perform better for any outcome.
Previous studies of anthropometric markers for CVD in children have being focused in alteration of central adiposity as a risk for cardiovascular disease (22, 23).
WC and WHtR showed better performance than BMI as predictors for cardiovascular disease in children populations (17, 20). A systematic review from cross sectional analyses, forty-four in adults, thirteen in children, supported these predictions.
Analysis revealed mean areas under ROC values of 0.704, 0.693 and 0.671 for WHtR, WC and BMI, respectively. The AUC for ROC analyses indicate that WHtR may be a more useful global clinical screening tool than WC, with a weighted mean boundary value of 0.5 (24).
The results presented in our study are consistent with most, but not all, studies
looking at the relationship between various anthropometric measures in childhood
and risk of developing chronic diseases. Freedman et al. (25), evaluated 2498 children (Bogalusa Heart Study), from 5 to 17 years, and found that the AUC for BMI and WHtR were similar to cardiometabolic risk and ranged from 0.85–0.86 for risk factor sum (Total-to-HDL cholesterol ratio, Triglycerides, LDL-cholesterol, HDL- cholesterol, Fasting insulin, Systolic blood pressure and Diastolic blood pressure).
However, waist-to-height ratio was slightly better in predicting concentrations of total- to-HDL cholesterol ratio and LDL cholesterol, similar to our study. Although WHtR may be preferred because of its simplicity, additional longitudinal data are needed to examine its relation to disease outcomes. It is important to note that there is no gold standard for cardiovascular risk diagnostic measures in childhood, which is largely the result of instability during this period of growth.
Our results suggest optimal cut-off points of 0.74 and 0.845 of BMI, for boys from 6-11 years and girls from 12-17 years, respectively. These findings may provide evidence for the early development of risk factors, even in children with lower BMI values than 1 z-score, likely due consequences of unhealthy eating and physical activity habits (26). Widespread change in children’s lifestyles may be affecting their body composition, which results in higher fat mass and lower fat-free mass, even when BMI falls in the normal range.
It’s important to notice the differences for that there is no universal cut-off point that can be used to identify alterations in WC for children of all ages (15). Contrary to WC, WHtR has the advantage of not requiring population specific reference tables as well as age and sex specific cutoffs.
Sensibility and specificity of each cut-off point among are different for sex and for age groups. For boys at the age of 6-11 years, BMI and WC were strongly sensible markers. This indicates that these cut-offs were adequate to screen true positives alterations in lipid profile. For boys at 12-17 years, WHtR showed maximum sensibility, with a strong specificity. This demonstrates how accurate the value of 0.5 is to identify risk of cardiovascular disease, as suggested in other studies (17, 24, 27). For girls of 6-11 years, BMI and WHtR showed high specificity. Both cut-off points were adequate to identify false negatives alterations in lipid profiles. For girls of 12-17 years, WC and WHtR showed very high sensibility, with a great screening capacity of true positives alterations in lipid markers.
Optimal cut-off values for WHtR ratio were similar to 0.5, except for girls at 12
to 17 years. They have all showed high sensibility, however, the latter had presented
low specificity. When comparing the optimum cut-off point of this anthropometric tool for boys for 12-17 with girls for 12-17, they’ve presented significant different values according to the Youden Index (1.757 vs. 1.404, respectively). This result shows that, even though they are the best performances for their specific gender and age group, 0.5 is a more reliable universal value (28), being able to distinguish the true positive and the false negative individuals more clearly. However, the 0.45 value shows that even a lower presence central obesity could identify alterations in the lipid profile.
Nevertheless, there were no statistical difference between the cutoff points of 0.45 and 0.50 for girls at this age according to the Youden Index (1.349 vs. 1.346, respectively). Solorzano et al. (29) alert that most of these girls should already be in post-pubertal stage, suggesting a consequence of more stable group height. It demonstrates that the value of 0.5 could misclassify girls with adequate WC for height, which have in fact compromised lipid status. Finally, these girls have been shown to be more vulnerable to CVD risk and require further attention for diagnosis.
While the results presented are robust, it is necessary and important to discuss potential limitations that may limit broader conclusions when interpreting the data. Even though our study showed higher absolute number of girls than boys in both age groups, the proportions between the two groups were the same.
Nevertheless, our study haven’t analyzed the biological state of maturation for this population. The literature demonstrates that 50% of girls would have menstruated at the mean age of 12.5 years, as our cut-off point for age groups was defined at 12 years old. Recent data suggests that excess adiposity during childhood may advance puberty in girls and delay puberty in boys. However, how obesity influences hormone aspects of pubertal development remains unclear (29).
In conclusion, based on our data, we’ve found that BMI, WC and WHtR were
adequate to identify cardiovascular risk for children from 6-17 years. WHtR showed
equivalent or better performance as the highest tools’ accuracy values, except for
boys from 6 to 11 years (BMI and WC). By using WHtR the diagnostic process is
facilitated, so it’s not necessary to be expressed by using Z-scores and a single cut-
off point (0,5) is possible to be used with the simple message of “keep your waist
circumference to than half of your height”. Overall, the application of this tool as an
indicator of cardiovascular risk in public health screening has not only statistical
advantages but is simpler.
6.7 Acknowledgements
This study was supported by FAPERGS (National Funding for Research - proc. no.
0700061). The contributors had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
6.8 References:
1. Roth J, Qiang X, Marban SL, Redelt H, Lowell BC. The obesity pandemic:
where have we been and where are we going? Obesity research. 2004 Nov;12 Suppl 2:88S-101S. PubMed PMID: 15601956.
2. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments.
Lancet. 2011 Aug 27;378(9793):804-14. PubMed PMID: 21872749.
3. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutrition reviews. 2012 Jan;70(1):3-21. PubMed PMID: 22221213. Pubmed Central PMCID: 3257829.
4. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012 Dec 15;380(9859):2224-60.
PubMed PMID: 23245609. Pubmed Central PMCID: 4156511.
5. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al.
Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014 Aug 30;384(9945):766-81. PubMed PMID: 24880830.
6. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al.
Heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation. 2014 Jan 21;129(3):e28-e292. PubMed PMID: 24352519.
7. Pagidipati NJ, Gaziano TA. Estimating deaths from cardiovascular disease: a review of global methodologies of mortality measurement. Circulation. 2013 Feb 12;127(6):749-56. PubMed PMID: 23401116. Pubmed Central PMCID: 3712514.
8. Morrison JA, Glueck CJ, Woo JG, Wang P. Risk factors for cardiovascular
disease and type 2 diabetes retained from childhood to adulthood predict adult
outcomes: the Princeton LRC Follow-up Study. International journal of pediatric
endocrinology. 2012;2012(1):6. PubMed PMID: 22507454. Pubmed Central PMCID:
3466140.
9. Zachariah JP, de Ferranti SD. NHLBI integrated pediatric guidelines: battle for a future free of cardiovascular disease. Future cardiology. 2013 Jan;9(1):13-22.
PubMed PMID: 23259472. Pubmed Central PMCID: 3705707.
10. Berenson GS, Srinivasan SR, Bao W, Newman WP, 3rd, Tracy RE, Wattigney WA. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. The New England journal of medicine. 1998 Jun 4;338(23):1650-6. PubMed PMID: 9614255.
11. McGill HC, Jr., McMahan CA, Herderick EE, Malcom GT, Tracy RE, Strong JP. Origin of atherosclerosis in childhood and adolescence. The American journal of clinical nutrition. 2000 Nov;72(5 Suppl):1307S-15S. PubMed PMID: 11063473.
12. Ferraro KF, Thorpe RJ, Jr., Wilkinson JA. The life course of severe obesity:
does childhood overweight matter? The journals of gerontology Series B, Psychological sciences and social sciences. 2003 Mar;58(2):S110-9. PubMed PMID:
12646600. Pubmed Central PMCID: 3358723.
13. Hu F. Measurements of adiposity and body composition. New York City;
Oxford University Press. 2008:1-498.
14. Bigaard J, Frederiksen K, Tjonneland A, Thomsen BL, Overvad K, Heitmann BL, et al. Waist circumference and body composition in relation to all-cause mortality in middle-aged men and women. International journal of obesity. 2005 Jul;29(7):778- 84. PubMed PMID: 15917857.
15. WHO. Waist circumference and waist-hip ratio: report of a WHO expert consultation 2008:8-11.
16. McCarthy HD, Ashwell M. A study of central fatness using waist-to-height ratios in UK children and adolescents over two decades supports the simple message--'keep your waist circumference to less than half your height'. International journal of obesity. 2006 Jun;30(6):988-92. PubMed PMID: 16432546.
17. Maffeis C, Banzato C, Talamini G, Obesity Study Group of the Italian Society of Pediatric E, Diabetology. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. The Journal of pediatrics. 2008 Feb;152(2):207-13. PubMed PMID: 18206690.
18. Lichtash CT, Cui J, Guo X, Chen YD, Hsueh WA, Rotter JI, et al. Body
adiposity index versus body mass index and other anthropometric traits as correlates
of cardiometabolic risk factors. PloS one. 2013;8(6):e65954. PubMed PMID:
23776578. Pubmed Central PMCID: 3679008.
19. Youden WJ. Index for rating diagnostic tests. Cancer. 1950 Jan;3(1):32-5.
PubMed PMID: 15405679.
20. Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, et al.
Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity. 2000 Nov;24(11):1453-8. PubMed PMID: 11126342.
21. Savva SC, Lamnisos D, Kafatos AG. Predicting cardiometabolic risk: waist-to- height ratio or BMI. A meta-analysis. Diabetes, metabolic syndrome and obesity : targets and therapy. 2013;6:403-19. PubMed PMID: 24179379. Pubmed Central PMCID: 3810792.
22. Bridger T. Childhood obesity and cardiovascular disease. Paediatrics & child health. 2009 Mar;14(3):177-82. PubMed PMID: 20190900. Pubmed Central PMCID:
2690549.
23. Schwandt P. Defining central adiposity in terms of clinical practice in children and adolescents. International journal of preventive medicine. 2011 Jan;2(1):1-2.
PubMed PMID: 21448396. Pubmed Central PMCID: 3063466.
24. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes:
0.5 could be a suitable global boundary value. Nutrition research reviews. 2010 Dec;23(2):247-69. PubMed PMID: 20819243.
25. Freedman DS, Kahn HS, Mei Z, Grummer-Strawn LM, Dietz WH, Srinivasan SR, et al. Relation of body mass index and waist-to-height ratio to cardiovascular disease risk factors in children and adolescents: the Bogalusa Heart Study. The American journal of clinical nutrition. 2007 Jul;86(1):33-40. PubMed PMID:
17616760.
26. Mikkila V, Rasanen L, Raitakari OT, Marniemi J, Pietinen P, Ronnemaa T, et al. Major dietary patterns and cardiovascular risk factors from childhood to adulthood.
The Cardiovascular Risk in Young Finns Study. The British journal of nutrition. 2007
Jul;98(1):218-25. PubMed PMID: 17367571.
27. Campagnolo PD, Hoffman DJ, Vitolo MR. Waist-to-height ratio as a screening tool for children with risk factors for cardiovascular disease. Annals of human biology.
2011 May;38(3):265-70. PubMed PMID: 21043577.
28. Arnaiz P, Grob F, Cavada G, Dominguez A, Bancalari R, Cerda V, et al.
[Waist-to-height ratio does not change with gender, age and pubertal stage in elementary school children]. Revista medica de Chile. 2014 May;142(5):574-8.
PubMed PMID: 25427013. La razon cintura estatura en escolares no varia con el genero, la edad ni la maduracion puberal.
29. Solorzano CR SCM. Obesity and the pubertal transition in girls and boys.
Reproduction. 2010;140(3):399-410.
6.9 Appendices:
Table I
Boys (M ± SD) Girls (M ± SD)
6-11 12-17 Total boys 6-11 12-17 Total girls
Age, years. 9.5 ±1.6 13.6 ±1.2 11.0 ±2.5 9.8 ±1.3 13.7 ±1.2 11.4 ±2.2 Weight, kg. 35.5 ±11.4 53 ±14 41.9 ± 15 36.8 ±10.6 51.6 ±12.8 42.7 ± 13.6 Height, cm. 136.9±10.7 158.5±11.7 144.8±15.2 138.8±10.4 156 ±7 145.7±12.5 BMI-for-age,
Z-score;
mediana ± IR
0.83 ±1.44 0.5 ±1.4 0.7 ± 1.42 0.68 ±1.2 0.38 ±1.1 0.56 ±1.2
WC, cm. 65.6 ±11.2 74.3 ±11.2 68.8 ± 12 66.8 ±10.7 74.4 ±11 69.8 ±11.4 WHtR; 0.48 ±0.06 0.47 ±0.06 0.47 ±0.06 0.48 ±0.06 0.48 ±0.06 0.48 ±0.06 M: mean; SD: standard deviation; IR:Interquartile range; BMI: Body-mass-index; kg: kilograms; cm:
centimeters; WC: Waist circumference; WHtR: Waist-to-height ratio.
Table II
Boys (M ± SD) Girls (M ± SD)
6-11 12-17 Total Boys 6-11 12-17 Total girls
TC, mg/Dl.
158.4 ±27.2 148.8 ±25.8 155 ±27 158.9 ±26.3 154.3 ±27.3 157 ±26.8
Tg, mg/dL.
69.3 ±31.2 78 ± 42.5 72.4 ±35.8 74.8 ±34 74 ±30.7 74.5 ±30.7
HDL-c, mg/dL.
48.7 ±12 43.9 ±9.4* 47 ±11.4 46.8 ±11.5 48.7 ±10.9† 47.6 ±11.3
LDL-c, mg/dL.
95.8 ±23.9 89.3 ±22.3 93.5 ±23.5 97.1 ±23 90.8 ±22 94,6 ±22.8 TC: Total cholesterol; Tg: Triglycerides; HDL-c: High density lipoprotein cholesterol; LDL-c: Low density lipoprotein cholesterol; M: mean; SD: Standard deviation; mg/dL: miligrams/deciliters. * p=0.03 vs 6-12 years; † p=0,001 vs boys 12-18 years.
Table III:
6-11 12-17
AUC ±SD min/max AUC ±SD min/max
Boys
BMI (Z-score) 0.735 ±0.05 0.62/0.84 0.855 ±0.05 0.75/0.96
WC (cm) 0.728 ±0.06 0.6/0.85 0.872 ±0.04 0.79/0.95
WHtR 0.719 ±0.06 0.60/0.84 0.902 ±0.04 0.82/0.98
Girls
BMI (Z-score) 0.704 ±0.06 0.58/0.82 0.635 ±0.08 0.47/0.8
WC (cm) 0.661 ±0.06 0.54/0.78 0.712 ±0.06 0.59/0.83
WHtR 0.696 ±0.05 0.58/0.80 0.702 ±0.06 0.58/0.82
BMI: Body-mass-index per age; cm: centimeters; WC: Waist circumference; WHtR: Waist-to-height ratio; AUC: Area under the curve; SD: Standard deviation; min: minimum; max: maximum
Figure 1:
Table IV:
BMI-for-age (Z-score) Waist circumference (cm)
Waist/height ratio
6-11 12-17 6-11 12-17 6-11 12-17
Boys
Cut-off 0.74 1.455 63.25 81.25 0.485 0.50
Sensibility 0.850 0.857 0.850 1 0.7 1
Specificity 0.574 0.811 0.623 0.797 0.73 0.757
Girls
Cut-off 1.715 0.845 73.75 71.25 0.499 0.445
Sensibility 0.583 0.667 0.5 0.917 0.583 1.0
Specificity 0.836 0.669 0.811 0.493 0.721 0.404
BMI: Body mass index; cm: centimeters.
